import os
import cv2
import numpy as np
import json
import random
from PIL import Image, ImageDraw, ImageFont
import asyncio

import requests
import base64
import gradio as gr
from IPython import embed

machine_number = 0
model = os.path.join(os.path.dirname(__file__), "models/eva/Eva_0.png")

MODEL_MAP = {
    "AI Model Rouyan_0": 'models/rouyan_new/Rouyan_0.png',
    "AI Model Rouyan_1": 'models/rouyan_new/Rouyan_1.png',
    "AI Model Rouyan_2": 'models/rouyan_new/Rouyan_2.png',
    "AI Model Eva_0": 'models/eva/Eva_0.png',
    "AI Model Eva_1": 'models/eva/Eva_1.png',
    "AI Model Simon_0": 'models/simon_online/Simon_0.png',
    "AI Model Simon_1": 'models/simon_online/Simon_1.png',
    "AI Model Xuanxuan_0": 'models/xiaoxuan_online/Xuanxuan_0.png',
    "AI Model Xuanxuan_1": 'models/xiaoxuan_online/Xuanxuan_1.png',
    "AI Model Xuanxuan_2": 'models/xiaoxuan_online/Xuanxuan_2.png',
    "AI Model Yaqi_0": 'models/yaqi/Yaqi_0.png',
    "AI Model Yaqi_1": 'models/yaqi/Yaqi_1.png',
    # "AI Model Yaqi_2": 'models/yaqi/Yaqi_2.png',
    # "AI Model Yaqi_3": 'models/yaqi/Yaqi_3.png',
    "AI Model Yifeng_0": 'models/yifeng_online/Yifeng_0.png',
    "AI Model Yifeng_1": 'models/yifeng_online/Yifeng_1.png',
    "AI Model Yifeng_2": 'models/yifeng_online/Yifeng_2.png',
    "AI Model Yifeng_3": 'models/yifeng_online/Yifeng_3.png',
}

def add_waterprint(img):

    h, w, _ = img.shape
    img = cv2.putText(img, 'Powered by OutfitAnyone', (int(0.3*w), h-20), cv2.FONT_HERSHEY_PLAIN, 2, (128, 128, 128), 2, cv2.LINE_AA)

    return img


def get_tryon_result(model_name, garment1, garment2, seed=1234):

    # model_name = "AI Model " + model_name.split("\\")[-1].split(".")[0] # windows
    # model_name = "AI Model " + model_name.split("/")[-1].split(".")[0] # linux
    # embed()
    # model_name = model_name['label']
    print(model_name)

    encoded_garment1 = cv2.imencode('.jpg', garment1)[1].tobytes()
    encoded_garment1 = base64.b64encode(encoded_garment1).decode('utf-8')

    if garment2 is not None:
        encoded_garment2 = cv2.imencode('.jpg', garment2)[1].tobytes()
        encoded_garment2 = base64.b64encode(encoded_garment2).decode('utf-8')
    else:
        encoded_garment2 = ''

    url = os.environ['OA_IP_ADDRESS']
    headers = {'Content-Type': 'application/json'}
    seed = random.randint(0, 1222222222)
    data = {
        "garment1": encoded_garment1,
        "garment2": encoded_garment2,
        "model_name": model_name,
        "seed": seed
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))
    print("response code", response.status_code)
    if response.status_code == 200:
        result = response.json()
        result = base64.b64decode(result['images'][0])
        result_np = np.frombuffer(result, np.uint8)
        result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
    else:
        print('server error!')

    
    final_img = add_waterprint(result_img)

    return final_img

# model_name="AI Model Rouyan_0"
# garment = np.uint8(Image.open('1.png'))
# embed()
# img = get_tryon_result(model_name, garment, None)

with gr.Blocks(css = ".output-image, .input-image, .image-preview {height: 400px !important} ") as demo:
    # gr.Markdown("# Outfit Anyone v0.9")
    gr.HTML(
        """
        <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
        <a href="https://github.com/HumanAIGC/OutfitAnyone" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
        </a>
        <div>
            <h1 >Outfit Anyone: Ultra-high quality virtual try-on for Any Clothing and Any Person</h1>
            <h4 >v0.9</h4>
            <h5 style="margin: 0;">If you like our project, please give us a star on Github to stay updated with the latest developments.</h5>
            <div style="display: flex; justify-content: center; align-items: center; text-align: center;>
                
            </div>
        </div>
        </div>
        """)
    gr.HTML(
        """
        <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
        <div>
            <h3>Models are fixed and cannot be uploaded or modified; we only support users uploading their own garments.</h3>
            <h4 style="margin: 0;">For a one-piece dress or coat, you only need to upload the image to the 'top garment' section and leave the 'lower garment' section empty.</h4>
        </div>
        </div>
        """)
    with gr.Row():
        with gr.Column():
            init_image = gr.Image(type="numpy", label="", value=model)
            name = gr.Label(value = "AI Model Eva_0", label="AI Model", visible=False)
            example = gr.Examples(inputs=[name, init_image],
                                  examples_per_page=3,
                                  examples= [[n, os.path.join(os.path.dirname(__file__), MODEL_MAP[n])] for n in MODEL_MAP.keys()],
                                  elem_id = 'example_table'
  )
        with gr.Column():
            with gr.Row():
                garment_top = gr.Image(sources='upload', type="numpy", label="top garment")
                example_top = gr.Examples(inputs=garment_top,
                                          examples_per_page=5,
                                          examples=[
                                            # os.path.join(os.path.dirname(__file__), "garments/top222.JPG"),
                                                    os.path.join(os.path.dirname(__file__), "garments/top5.png"),
                                                    os.path.join(os.path.dirname(__file__), "garments/top333.png"),
                                                    os.path.join(os.path.dirname(__file__), "garments/dress1.png"),
                                            # os.path.join(os.path.dirname(__file__), "garments/dress2.png"),
                                                            ])
                garment_down = gr.Image(sources='upload', type="numpy", label="lower garment")
                example_down = gr.Examples(inputs=garment_down,
                                           examples_per_page=5,
                                           examples=[os.path.join(os.path.dirname(__file__), "garments/bottom1.png"),
                                                    #  os.path.join(os.path.dirname(__file__), "garments/bottom2.PNG"),
                                                    #  os.path.join(os.path.dirname(__file__), "garments/bottom3.JPG"),
                                                     os.path.join(os.path.dirname(__file__), "garments/bottom4.PNG"),
                                                     os.path.join(os.path.dirname(__file__), "garments/bottom5.png"),
                                                            ])

            run_button = gr.Button(value="Run")
        with gr.Column():
            gallery = gr.Image()

            run_button.click(fn=get_tryon_result, 
                             inputs=[
                                    name,
                                    garment_top,
                                    garment_down,
                                    ], 
                             outputs=[gallery],)
        

    # Examples
    gr.Markdown("## Examples")
    with gr.Row():
        reference_image1  = gr.Image(label="model", scale=1, value="examples/basemodel.png")
        reference_image2  = gr.Image(label="garment", scale=1, value="examples/garment1.jpg")
        reference_image3  = gr.Image(label="result", scale=1, value="examples/result1.png")
    gr.Examples(
        examples=[
            ["examples/basemodel.png", "examples/garment1.png", "examples/result1.png"],
            ["examples/basemodel.png", "examples/garment2.png", "examples/result2.png"],
            ["examples/basemodel.png", "examples/garment3.png", "examples/result3.png"],
        ],
        inputs=[reference_image1, reference_image2, reference_image3],
        label=None,
    )

if __name__ == "__main__":
    ip = requests.get('http://ifconfig.me/ip', timeout=1).text.strip()
    print("ip address alibaba", ip)
    demo.queue(max_size=10)
    demo.launch(server_name='192.168.1.4', server_port=7860)

